With the popularity of speech input function and application on intelligent terminals such as mobile phones and the like, there are more and more demands of users for the use of speech input on intelligent terminals such as mobile phones and the like, and higher requirements have also been put forward for user personalized information, especially for the accuracy rate of recognition of contacts in an address book. Unfortunately, due to the limitation in language model training mode and recognition method, the traditional continuous speech recognition system may be unable to provide a correct word for the Chinese speech signal in which there are polyphone characters, and especially in name information recognition, the recognition accuracy rate is subject to further restrictions, and the main problems are as follows:
1. there are a large number of common names in Chinese, for which name words are usually treated as unlisted words in the dictionary of continuous speech recognition, and that results in an extremely limited number of names covered in a training corpus;
2. secondly, there are a lot of homophones in Chinese names: common names have dozens of or even more combinations of Chinese characters; and
3. for each user, there may still be some names which are not so commonly used in the names of contacts in a user-specific personalized address book, namely each personalized name list cannot be uniformly covered in the training corpus.
Based on the above reasons, the language model for continuous speech recognition in the prior art cannot well simulate name words, especially personalized words of names of user contacts, and the name recognition effects are also often significantly worse than the recognition effect of other contents. Obviously, how to improve the user personalized information in continuous speech recognition, especially the recognition accuracy rate of name information has become an urgent problem to be solved for a continuous speech recognition system.